1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
1/10/2024 Electricidad 88000 Andrés NA
1/10/2024 Comida 22895 Andrés piwen
2/10/2024 Viaje Brasil 277776 Tami Cuota que faltaba viaje Brasil
5/10/2024 Comida 50577 Tami Supermercado
6/10/2024 Agua 11723 Andrés NA
9/10/2024 Diosi 14990 Andrés comida diosi nyd 1.5k
12/10/2024 Comida 70144 Tami Supermercado
19/10/2024 Comida 62351 Tami Supermercado
24/10/2024 Electrodomésticos/mantención casa 13500 Andrés lona quitasol
24/10/2024 VTR 21990 Andrés NA
25/10/2024 Comida 8350 Tami Supermercado
27/10/2024 Comida 78084 Tami Supermercado
28/10/2024 Diosi 15000 Andrés NA
29/10/2024 Electricidad 50227 Andrés NA
31/10/2024 Electrodomésticos/mantención casa 13264 Andrés lona sombreadora
31/10/2024 Agua 12000 Andrés NA
4/11/2024 Comida 64915 Tami Supermercado
7/11/2024 Comida 34229 Andrés piwen
8/11/2024 Assistcard viaje 44836 Tami Assistcard viaje
9/11/2024 Comida 35916 Tami Supermercado
15/11/2024 Comida 12576 Tami Supermercado
17/11/2024 Comida 91203 Tami Supermercado
18/11/2024 Diosi 49500 Tami Veterinaria
23/11/2024 Comida 50929 Tami Supermercado
26/11/2024 Enceres 12990 Tami Confort 48 rollos
27/11/2024 Transporte 21780 Tami Transvip aeropuerto
27/11/2024 Electricidad 45000 Andrés Eléctrico
30/11/2024 Comida 62899 Tami Supermercado
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 1.0001e+09   2    4.9411 0.0074 ** 
## lag_depvar    2.5880e+11   1 2557.3413 <2e-16 ***
## Residuals     7.9441e+10 785                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838 -2027.736 16485.41 0.1592822
## 2-0 31805.987 23459.768 40152.21 0.0000000
## 2-1 24577.149 19727.226 29427.07 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
## 30   28331.57             0   28706.00
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## 708  98395.14             2   95424.29
## 709 115594.71             2   98395.14
## 710 114267.57             2  115594.71
## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 765  42208.71             2   44524.86
## 766 166486.57             2   42208.71
## 767 171565.29             2  166486.57
## 768 200415.71             2  171565.29
## 769 204498.14             2  200415.71
## 770 197558.86             2  204498.14
## 771 195266.57             2  197558.86
## 772 203144.29             2  195266.57
## 773  85493.71             2  203144.29
## 774  74721.57             2   85493.71
## 775  36232.14             2   74721.57
## 776  40161.71             2   36232.14
## 777  40629.86             2   40161.71
## 778  45663.71             2   40629.86
## 779  39252.29             2   45663.71
## 780  39618.57             2   39252.29
## 781  39438.43             2   39618.57
## 782  44650.71             2   39438.43
## 783  38626.71             2   44650.71
## 784  38280.43             2   38626.71
## 785  44134.14             2   38280.43
## 786  47596.43             2   44134.14
## 787  45598.43             2   47596.43
## 788  42564.29             2   45598.43
## 789  45699.14             2   42564.29
## 790  49553.86             2   45699.14
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   633 54040.25 22739.689
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## [764]  42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29  85493.71  74721.57  36232.14  40161.71  40629.86  45663.71
## [778]  39252.29  39618.57  39438.43  44650.71  38626.71  38280.43  44134.14
## [785]  47596.43  45598.43  42564.29  45699.14  49553.86
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2023.897867   4042.519522   -540.168680   2436.253821  -2973.778075 
##             7             8             9            10            11 
##    517.238971  -5658.064194  -1185.195778  -3963.250311   -412.355488 
##            12            13            14            15            16 
##  -4934.891849  -1601.219142   -891.597940    384.977556  -3237.074103 
##            17            18            19            20            21 
##   -370.357169  -2123.590712   6611.308571  -1529.441691  -1207.621964 
##            22            23            24            25            26 
##   1476.806536  -1187.370542    234.569247   1694.266416  -7104.654910 
##            27            28            29            30            31 
##    951.301688   8194.490299    412.393897    -19.768831  -2405.947996 
##            32            33            34            35            36 
##   1573.329167   4568.250253   1118.788414   2383.027014  -1877.800047 
##            37            38            39            40            41 
##   4600.730184   4299.512471  -2282.980987  -2985.754218  -1111.255547 
##            42            43            44            45            46 
## -10741.447164   7298.964211   2559.048119   1366.074777   8103.231219 
##            47            48            49            50            51 
##    677.163814   6520.274932   6701.109613  -5899.976315  -4805.572121 
##            52            53            54            55            56 
##  -5064.278926  -7928.003766   6137.415981  -4075.516320  -4889.741632 
##            57            58            59            60            61 
##   3864.169568    891.613072    -29.665820    144.344366  -4994.756757 
##            62            63            64            65            66 
##  18132.697914   3629.489127  -3659.147033   5917.278760   7331.702631 
##            67            68            69            70            71 
##  14621.589102   1665.056292 -13238.703783  -1316.776620   4635.502308 
##            72            73            74            75            76 
##  -4911.400555  -4409.716802 -10497.859323   2476.318246  -5393.552030 
##            77            78            79            80            81 
##   1074.328409  -6857.884082    560.731134  -2342.893623  -2681.356164 
##            82            83            84            85            86 
##  -3918.319761   -521.886108   2328.875721   3773.010025    482.366814 
##            87            88            89            90            91 
##   -480.289754    200.717104   4305.279432  -1164.176336   1150.877315 
##            92            93            94            95            96 
##  -2065.562361  -1043.344228    179.364464    276.144196  -7483.136938 
##            97            98            99           100           101 
##   2399.651802  -8597.805421  -2928.149125  -4025.570587  -1720.466112 
##           102           103           104           105           106 
##  -1245.205172   3196.480965  -2331.432872   2605.601003  -1150.737766 
##           107           108           109           110           111 
##    978.293806   2593.022237  -3151.687266  -4717.660464   -840.977034 
##           112           113           114           115           116 
##   1912.509362  11699.611522  -1248.492348   2664.564643   4256.855271 
##           117           118           119           120           121 
##   3493.429167  -1111.301233  -4725.132850  -3727.198820   2320.706559 
##           122           123           124           125           126 
##  -1733.971109   1341.002674   8857.541636    838.094228    121.835267 
##           127           128           129           130           131 
##  -2528.851179   2650.911963   7046.419268   1000.438243  -8510.804841 
##           132           133           134           135           136 
##   1747.384656   4132.285775  -3170.694867  -1422.574027   -855.070753 
##           137           138           139           140           141 
##  -3880.107895   1186.713884   -493.361738  -2911.246469   1723.046331 
##           142           143           144           145           146 
##  -1878.433101  -7825.168140   2050.591676  -3471.932723   2112.345084 
##           147           148           149           150           151 
##   -250.693992   1029.150817   -354.983596   1356.262865   1188.842404 
##           152           153           154           155           156 
##   3357.324828  -4864.395554  -1172.104566  -3232.628523   5962.592557 
##           157           158           159           160           161 
##   9746.033591  -3691.661128  -5036.048524   3349.459546    -65.232389 
##           162           163           164           165           166 
##   2434.565377  -6176.281700  -7008.027592   3901.611467  17130.548667 
##           167           168           169           170           171 
##   3338.438963   -692.131751  -2737.781818  -1391.582637   3305.066787 
##           172           173           174           175           176 
##   -518.333446  -8364.530180   2585.994674   4042.931242    336.638227 
##           177           178           179           180           181 
##   8460.969600  -9549.857225  -3758.502757 -11026.591974 -11507.885462 
##           182           183           184           185           186 
##    980.289686   9032.967985  -1706.970031   5652.041205   6267.605499 
##           187           188           189           190           191 
##  12858.380554   8108.147115  -4399.758384   2135.562590  10035.437171 
##           192           193           194           195           196 
##  -1993.780737  -2789.486636 -10619.074034  -6682.136794    926.276357 
##           197           198           199           200           201 
##  -5542.814413 -10096.096344   5100.323201  -3362.304242  -2001.719895 
##           202           203           204           205           206 
##  -1091.622948   6206.669207   9579.125377    253.817987   2599.144780 
##           207           208           209           210           211 
##   2767.422300   5449.234427  12489.472609  -6052.926147 -11645.262372 
##           212           213           214           215           216 
##  -5989.686395 -10897.882090  -5364.552574   1246.147173 -13295.890102 
##           217           218           219           220           221 
##  16127.492611   7511.192363   1212.882565  26369.898249  12153.243093 
##           222           223           224           225           226 
##   6941.195451  13625.245905  -4335.132406  -2143.886802   3387.267639 
##           227           228           229           230           231 
##    -28.954381   2365.767945   8627.983966   5446.249903  -2289.732361 
##           232           233           234           235           236 
##  -2197.493544   9067.923196 -11877.969707  -7623.638664  -8863.612611 
##           237           238           239           240           241 
## -10405.753008   2792.638423   1059.326002  -8593.475321  -9270.417815 
##           242           243           244           245           246 
##   8829.166867  -8052.971974   2213.232885 -10583.524938  -4318.329263 
##           247           248           249           250           251 
##   1161.850845    734.176682 -12591.261647   3389.147020   1794.600125 
##           252           253           254           255           256 
##   3934.591118   1844.482838  -1458.377360  10841.447495  20555.956587 
##           257           258           259           260           261 
##   2823.300228  -4648.929164   3745.768437  -2064.217239   3375.547872 
##           262           263           264           265           266 
##  -5218.269040 -11245.055748  -5052.699323   -834.951650  -5501.358394 
##           267           268           269           270           271 
##   8475.482516  -4606.883975   3871.949076  -2436.411474   4105.570993 
##           272           273           274           275           276 
##    371.545777   6963.412831  -1770.326540  11671.505531  -4969.015077 
##           277           278           279           280           281 
##   1354.869132   -745.305298   7481.866055  -5445.663044  -3101.064371 
##           282           283           284           285           286 
## -11619.972849  -2993.379411  18338.197899   7412.986595   2344.095457 
##           287           288           289           290           291 
##  -1022.059889    519.141224   6013.015447   6482.757702 -19186.507116 
##           292           293           294           295           296 
## -11487.657517  -8431.966595   9380.280596   2755.866739  -1504.653175 
##           297           298           299           300           301 
##  27080.746210   9657.377101   4468.171515   9079.604976   2398.729547 
##           302           303           304           305           306 
##  -1486.390456   7458.690367 -24746.857005  -3891.504404   -514.103217 
##           307           308           309           310           311 
##  -7302.012638  -4278.147574   2641.067769  -9492.906373  -3497.810055 
##           312           313           314           315           316 
##  -8443.732633   1334.433676  -3392.419246   1813.355800  -4330.627086 
##           317           318           319           320           321 
##  27206.122638  -1086.702381   2933.650833  10463.561256   5187.366165 
##           322           323           324           325           326 
##  31966.486454   4592.845586 -21450.600208   1376.115422    699.481517 
##           327           328           329           330           331 
##  -6868.737725  -2103.734990 -33623.168618    690.859338  -2498.609514 
##           332           333           334           335           336 
##   -283.024452  -3360.708395   3901.074761   -642.554828  -7159.919981 
##           337           338           339           340           341 
##  -3300.261599  -2368.602466  -7854.014088   3700.735338  -1547.062646 
##           342           343           344           345           346 
##  -1915.523208  -1171.755760     -4.336529    292.993508  -1815.791545 
##           347           348           349           350           351 
##  -9643.681214 -13376.048511   2187.395355  -4468.493136  -3797.216832 
##           352           353           354           355           356 
##  -6115.275909   1627.711973   1240.086964   2590.332886  -3952.583419 
##           357           358           359           360           361 
##   -696.366777    489.604537   6813.906448     38.724299   -281.767263 
##           362           363           364           365           366 
##   2336.002439  -3011.427851  -1127.226524  -8990.432728  -4834.453754 
##           367           368           369           370           371 
##  -6403.777624  -5118.310299  -7406.777837   4883.746026    203.748671 
##           372           373           374           375           376 
##   6940.627104  -7856.831027  -2452.969707  -3573.800192  -2643.934269 
##           377           378           379           380           381 
## -12630.348373   1778.356434 -10780.272905   5586.835615   9184.951358 
##           382           383           384           385           386 
##   2919.112222  -2627.854453   1381.171084   6507.289811  11137.070484 
##           387           388           389           390           391 
##  -6133.001562  -5667.438469   -438.529987   8281.002605   1490.298312 
##           392           393           394           395           396 
##  10889.763561 -10262.407925   2440.812680    368.171820    217.818798 
##           397           398           399           400           401 
##   -997.465890   -899.970965 -14818.096992   8270.335795  -1471.590639 
##           402           403           404           405           406 
##  -1654.043888   6709.172038  -8238.211783  -1558.832494  -2784.593214 
##           407           408           409           410           411 
##  -6057.499462  -3067.258761  -4112.758648  -8934.772829   5991.925702 
##           412           413           414           415           416 
##   1458.089108  -7571.356747  -7859.052913  14084.311644   3593.201881 
##           417           418           419           420           421 
##   4241.151271  -8314.786838  -4982.356284  -2816.205911   2615.394538 
##           422           423           424           425           426 
## -14233.691342  -2946.498731  -9247.751716   2900.688475   6836.260260 
##           427           428           429           430           431 
##   6386.923659  -4217.789543  -4334.392675  -4918.948191  -1967.793142 
##           432           433           434           435           436 
##  -5887.966725  -6781.523899  -6080.888632  -1506.737998   -967.912034 
##           437           438           439           440           441 
##  -5104.189543   2464.726416   4695.688628  -5236.463249  -2320.802124 
##           442           443           444           445           446 
##   1415.331370  -4015.342470   2668.719483  -6767.719920 -12274.043920 
##           447           448           449           450           451 
##  -4624.490380   9540.975871  -2196.599688   4591.741370  -6063.169974 
##           452           453           454           455           456 
##  -1292.833446    212.155497   2845.922240 -12469.413143   3222.107883 
##           457           458           459           460           461 
##  -6873.488106   6374.754205   2824.673465   2299.640770  -4068.639101 
##           462           463           464           465           466 
##   1886.176464   -227.216004   1570.964378   -753.698393   3119.413548 
##           467           468           469           470           471 
##  -2887.098936   5570.988304  -7203.373630  -3188.725802  -2414.828749 
##           472           473           474           475           476 
##  -4863.395847   2817.051523   7599.583709  -6254.365363   1276.460305 
##           477           478           479           480           481 
##  -6394.910931  -3032.057245   1834.390160 -13121.152774  -9891.026040 
##           482           483           484           485           486 
##  -1303.420329    -88.263779  -1083.058446  -1469.434716  -9716.669297 
##           487           488           489           490           491 
##  10996.154618   6072.669362   7222.285303  -5671.998446   5156.905407 
##           492           493           494           495           496 
##   9055.983532   5774.741983 -13775.799926 -10799.005268  -3624.469476 
##           497           498           499           500           501 
##  -1276.637213   -694.584111  -7798.438129    468.957535   4135.759620 
##           502           503           504           505           506 
##   5331.002190    453.742070   -130.366483  -7451.086101    389.933189 
##           507           508           509           510           511 
##  -5234.377157   1666.148447  -1475.933640  -8335.293365   -747.288126 
##           512           513           514           515           516 
##  -2824.263019   -732.525403   1182.564138  -9657.781812  -7891.892200 
##           517           518           519           520           521 
##  24184.149859   9602.875444   5612.475138  -5625.217465   2538.516683 
##           522           523           524           525           526 
##  16749.849058  11131.814523 -24531.482573  -5318.955507  -3966.025926 
##           527           528           529           530           531 
##   4357.315682   -592.998839 -11336.348783   4208.525110  13703.218282 
##           532           533           534           535           536 
##  -5247.606208   4128.834568   5291.536090  -2076.010936  -4813.160162 
##           537           538           539           540           541 
##  -7321.186413  -2312.947250   8119.344107   -114.689079  -8381.900520 
##           542           543           544           545           546 
##   1614.126427   -811.066097    156.796986 -11242.869494 -11224.974836 
##           547           548           549           550           551 
##   1921.210224   6864.992627  -1495.455532    660.720818  -7904.988984 
##           552           553           554           555           556 
##   8411.472080    708.395240 -12149.108715   9008.368358   8455.133034 
##           557           558           559           560           561 
##   -145.706303   4608.815614  -3839.517180  13862.337531  21190.033552 
##           562           563           564           565           566 
##  -6865.576921 -10034.817725   6476.664596    -95.888883   3140.774282 
##           567           568           569           570           571 
##  -7697.953208 -17585.001870   6471.634128   6213.185063   1653.807754 
##           572           573           574           575           576 
##   2845.382773   1507.667401  -2429.851224  14467.305565  -9961.454133 
##           577           578           579           580           581 
##  -6505.713543   8482.061575   2591.614716  -6820.186284   7269.130290 
##           582           583           584           585           586 
##  -4070.433424  -3023.684785  15470.449509 -14799.428667   8199.745780 
##           587           588           589           590           591 
##   -193.736075  -6471.431908   -980.335567     31.473496 -10873.093508 
##           592           593           594           595           596 
##   1625.694566  -7326.465456   2914.986507   8695.135362  -7714.474417 
##           597           598           599           600           601 
##   5670.710917   2525.570921   6634.188604  -3440.753585   5917.012134 
##           602           603           604           605           606 
##  -8555.272234   2039.928761   1046.034654   2910.908698   1256.685817 
##           607           608           609           610           611 
##    156.129274  -6049.425491   7865.081661  -1427.067150  -2807.475217 
##           612           613           614           615           616 
##  -3671.362166  -8428.606586  11794.638267   4718.569035  -9550.952025 
##           617           618           619           620           621 
##  11433.896402   5808.219053  -5835.077398  26129.133664 -13159.847725 
##           622           623           624           625           626 
##  -7043.060831   2931.063494  -4393.921836 -10804.395123  11130.859857 
##           627           628           629           630           631 
## -21849.465723  -2538.933637   8554.313671  10972.817932  -1762.730122 
##           632           633           634           635           636 
##  33082.786130  -6920.105183   5426.483818   5096.999269  -2579.550821 
##           637           638           639           640           641 
##  -5633.196390  -2195.304190 -12670.693327  -2426.874039  -2061.804548 
##           642           643           644           645           646 
##  -2689.287700  -3018.597614   1663.244226   4268.444072  16782.427157 
##           647           648           649           650           651 
##  18303.040948    566.004540   4481.160506  10297.444562  19808.504389 
##           652           653           654           655           656 
##    345.448790 -28438.848611  -1585.267965  -2520.021742   1656.909222 
##           657           658           659           660           661 
##  -3403.213072 -10814.173185   1515.620947   4070.740414  -1177.662407 
##           662           663           664           665           666 
##  12866.383478   1148.810483   1602.705449 -11902.643566   1213.969876 
##           667           668           669           670           671 
##   1018.450595  -5337.251692  -7561.228957   1941.384626  -3846.370715 
##           672           673           674           675           676 
##   2549.523046  -3514.875982  -9463.243579  -8406.229549  -3059.804590 
##           677           678           679           680           681 
##     89.551867   2753.664391    602.786099  -3945.003486  -1919.801814 
##           682           683           684           685           686 
##  -1429.706047  -8355.511305   4555.237645  -2358.058854  -1512.250519 
##           687           688           689           690           691 
##    472.367427  10731.594326   9690.572351  10435.914135  -9876.391931 
##           692           693           694           695           696 
##  -3728.707974  -3299.912113   5721.981207 -10551.102539  -8043.627041 
##           697           698           699           700           701 
##  -8721.822723  -6364.964362  -4819.615895   3006.037858  -4495.788298 
##           702           703           704           705           706 
##  -1987.015435   4130.990826  30997.138954   9348.621225  23268.259924 
##           707           708           709           710           711 
##   1483.902996   8140.498734  22741.283296   6368.644078 -18384.710208 
##           712           713           714           715           716 
##   4681.640675  -5581.281748   -224.483239    360.434927 -17382.942510 
##           717           718           719           720           721 
##  -5356.884488   3248.404639  -3104.671520 -13066.107152   4207.116196 
##           722           723           724           725           726 
##  -5636.639552    667.085876  -4013.332928 -12523.095353   1304.529777 
##           727           728           729           730           731 
##  -1935.795359  -9847.302128  17207.841912   1688.788661  -2812.415171 
##           732           733           734           735           736 
##   5628.294595  -8725.294703   -807.470478   8053.890678 -15447.023362 
##           737           738           739           740           741 
##  -5987.639014   7336.410809  -4868.271120     81.467607   1746.166195 
##           742           743           744           745           746 
##  -2041.340897  -5252.334575   6333.306489  -6364.058881  22616.590902 
##           747           748           749           750           751 
##   7716.600771  -2064.258795  -7399.881415  23315.974072  -4416.805500 
##           752           753           754           755           756 
##   1280.898649 -14535.469747  56015.295296  26771.325015  14931.247635 
##           757           758           759           760           761 
## -10810.455739  10467.414567   7174.528212   5672.214289 -46522.595071 
##           762           763           764           765           766 
## -16250.044189    903.901867  -2582.252107  -3521.147895 122782.778273 
##           767           768           769           770           771 
##  19148.199604  43555.972286  22401.201204  11890.770449  15668.694007 
##           772           773           774           775           776 
##  25551.608045 -98990.072392  -6846.206460 -35912.595810   1685.986496 
##           777           778           779           780           781 
##  -1283.302381   3341.042137  -7473.803092  -1499.056397  -1999.611330 
##           782           783           784           785           786 
##   3370.256143  -7213.242684  -2289.974418   3866.656744   2208.347602 
##           787           788           789           790 
##  -2818.321300  -4104.693763   1684.310054   2796.776841 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17245.39  20096.48  24356.31  24073.89  26430.49  23759.48  24476.78  19702.34 
##        10        11        12        13        14        15        16        17 
##  19438.54  16777.64  17556.18  14281.08  14332.31  14997.88  16696.79  15014.50 
##        18        19        20        21        22        23        24        25 
##  16050.59  15423.26  22515.44  21598.19  21077.34  22969.94  22295.00  22948.45 
##        26        27        28        29        30        31        32        33 
##  24796.94  18716.98  20445.51  28293.61  28351.34  28023.81  25649.96  27054.32 
##        34        35        36        37        38        39        40        41 
##  30902.64  31251.54  32662.66  30169.84  34143.49  37355.98  34408.04  31214.54 
##        42        43        44        45        46        47        48        49 
##  30060.73  20627.32  28156.38  30596.21  31686.91  38534.41  38028.30  42696.89 
##        50        51        52        53        54        55        56        57 
##  46938.98  39626.86  34187.85  29203.72  22338.73  28637.37  25213.31  21505.83 
##        58        59        60        61        62        63        64        65 
##  25920.24  27181.52  27478.94  27891.33  23756.59  40370.65  42217.15  37456.58 
##        66        67        68        69        70        71        72        73 
##  41669.30  46591.70  57274.52  55285.56  40508.49  38010.93  41032.97  35325.29 
##        74        75        76        77        78        79        80        81 
##  30771.29  21461.97  24667.84  20587.96  22676.88  17565.41  19583.61  18809.07 
##        82        83        84        85        86        87        88        89 
##  17835.46  15901.74  17181.27  20794.28  25218.06  26209.29  26234.28  26851.86 
##        90        91        92        93        94        95        96        97 
##  30982.60  29811.55  30812.28  28874.06  28072.78  28441.43  28848.57  22417.21 
##        98        99       100       101       102       103       104       105 
##  25436.38  18457.29  17311.86  15349.89  15650.06  16328.38  20807.15  19889.40 
##       106       107       108       109       110       111       112       113 
##  23405.31  23194.99  24873.41  27754.12  25248.80  21687.41  21963.20  24613.10 
##       114       115       116       117       118       119       120       121 
##  35492.49  33682.86  35522.86  38525.29  40483.87  38169.13  32983.06  29319.44 
##       122       123       124       125       126       127       128       129 
##  31405.11  29682.71  30865.89  38476.05  38118.02  37178.28  34037.52  35821.15 
##       130       131       132       133       134       135       136       137 
##  41226.42  40665.95  31855.62  33122.14  36316.27  32722.00  31107.07  30190.82 
##       138       139       140       141       142       143       144       145 
##  26743.14  28159.50  27928.82  25611.95  27639.15  26262.03  19855.41  22890.08 
##       146       147       148       149       150       151       152       153 
##  20713.80  23694.98  24235.71  25828.27  26010.59  27667.01  28969.53  32005.82 
##       154       155       156       157       158       159       160       161 
##  27469.82  26731.77  24283.69  30185.82  41712.09  40040.05  37401.40  42428.52 
##       162       163       164       165       166       167       168       169 
##  43839.01  47259.57  42719.31  38020.10  43452.74  59777.13  61992.27  60404.21 
##       170       171       172       173       174       175       176       177 
##  57225.58  55622.65  58328.90  57351.67  49633.29  52460.64  56208.36  56244.60 
##       178       179       180       181       182       183       184       185 
##  63383.14  53872.50  50619.02  41415.17  32943.00  36456.03  46573.26  46028.53 
##       186       187       188       189       190       191       192       193 
##  51989.39  57742.19  68539.85  73829.90  67516.01  67709.71  74789.64  70460.20 
##       194       195       196       197       198       199       200       201 
##  65976.93  55206.14  49228.15  50654.39  46243.10  38401.25  44834.73  43059.72 
##       202       203       204       205       206       207       208       209 
##  42697.19  43176.19  49979.45  58880.75  58509.86  60237.01  61895.05  65691.38 
##       210       211       212       213       214       215       216       217 
##  75170.78  67242.83  55415.83  50017.31  41001.41  37955.00  41072.89  31079.51 
##       218       219       220       221       222       223       224       225 
##  48076.09  55406.83  56309.96  79106.33  86611.52  88617.47  96219.13  87157.74 
##       226       227       228       229       230       231       232       233 
##  81148.02  80729.38  77374.80  76535.16  81278.61  82644.73  77072.64  72279.08 
##       234       235       236       237       238       239       240       241 
##  77940.40  64570.07  56595.76  48535.47  40135.65  44333.25  46488.90  39930.70 
##       242       243       244       245       246       247       248       249 
##  33601.69  43898.11  38137.20  42078.24  34331.61  33035.72  36695.97  39523.69 
##       250       251       252       253       254       255       256       257 
##  30340.71  36286.83  40093.41  45295.23  48017.23  47509.12  57824.04  75344.99 
##       258       259       260       261       262       263       264       265 
##  75159.79  68461.37  69945.22  66160.88  67608.98  61358.20  50618.27  46640.24 
##       266       267       268       269       270       271       272       273 
##  46849.93  42951.37  51767.46  48035.48  52187.84  50301.86  54374.74  54671.16 
##       274       275       276       277       278       279       280       281 
##  60696.76  58327.78  68013.87  61930.42  62140.73  60487.56  66238.23  59960.21 
##       282       283       284       285       286       287       288       289 
##  56519.40  46057.52  44452.09  61707.73  67245.33  67655.35  65069.43  64155.56 
##       290       291       292       293       294       295       296       297 
##  68161.96  72077.51  53048.23  43136.82  37139.72  47475.13  50721.37  49834.11 
##       298       299       300       301       302       303       304       305 
##  74063.34  80016.83  80685.40  85304.13  83500.25  78523.74  81995.29  56859.93 
##       306       307       308       309       310       311       312       313 
##  53115.96  52795.30  46577.00  43782.65  47390.91  39932.95  38653.30  33207.42 
##       314       315       316       317       318       319       320       321 
##  36997.13  36177.36  40014.06  37995.73  63817.27  61655.49  63281.30  71290.35 
##       322       323       324       325       326       327       328       329 
##  73680.94  99197.44  97572.89  73370.03  72166.23  70521.31  62462.02  59580.31 
##       330       331       332       333       334       335       336       337 
##  29487.57  33180.18  33620.31  35943.42  35283.35  41058.27  42135.35  37376.40 
##       338       339       340       341       342       343       344       345 
##  36589.75  36716.59  32029.12  38036.35  38700.67  38959.47  39836.48  41624.86 
##       346       347       348       349       350       351       352       353 
##  43449.36  43200.68  36135.62  26690.46  32042.49  30901.93  30491.42  28104.57 
##       354       355       356       357       358       359       360       361 
##  32789.91  36549.38  41019.15  39205.65  40467.68  42609.09  50014.56  50565.91 
##       362       363       364       365       366       367       368       369 
##  50767.85  53234.43  50714.37  50158.15  42793.17  39986.06  36157.74  33933.35 
##       370       371       372       373       374       375       376       377 
##  29985.68  37283.68  39573.80  47470.26  41433.54  40879.94  39415.22  38947.35 
##       378       379       380       381       382       383       384       385 
##  29802.36  34406.84  27448.88  35679.62  46027.03  49597.43  47868.40  49862.85 
##       386       387       388       389       390       391       392       393 
##  56091.64  65590.29  58792.15  53252.67  52981.00  60370.84  60894.95  69575.69 
##       394       395       396       397       398       399       400       401 
##  58666.19  60235.26  59794.75  59277.89  57762.69  56522.53  43262.66  51860.30 
##       402       403       404       405       406       407       408       409 
##  50859.33  49824.11  56234.35  48766.40  48076.59  46400.93  42072.12  40901.19 
##       410       411       412       413       414       415       416       417 
##  38962.34  33048.22  40932.05  43862.50  38527.34  33608.69  48501.23  52351.42 
##       418       419       420       421       422       423       424       425 
##  56286.22  48744.78  45062.92  43737.03  47328.55  35731.36  35460.18  29710.88 
##       426       427       428       429       430       431       432       433 
##  35308.60  43647.93  50549.79  47310.68  44375.23  41296.08  41184.11  37656.95 
##       434       435       436       437       438       439       440       441 
##  33789.89  31020.02  32598.34  34450.33  32452.13  37325.17  43539.46  40287.23 
##       442       443       444       445       446       447       448       449 
##  39992.81  43003.49  40886.57  44881.72  40121.90  31141.49  29977.31  41350.31 
##       450       451       452       453       454       455       456       457 
##  41031.40  46690.60  42320.55  42670.70  44293.51  48016.98  37876.89  42733.06 
##       458       459       460       461       462       463       464       465 
##  38149.82  45729.61  49254.64  51878.92  48603.82  50947.93  51149.75  52899.27 
##       466       467       468       469       470       471       472       473 
##  52396.16  55344.10  52668.58  57726.95  50977.30  48584.83  47168.97  43788.52 
##       474       475       476       477       478       479       480       481 
##  47549.99  55023.94  49442.97  51148.63  45930.06  44306.75  47143.72  36542.88 
##       482       483       484       485       486       487       488       489 
##  30095.28  31967.26  34667.77  36159.86  37127.10  30758.85  43306.90  49976.57 
##       490       491       492       493       494       495       496       497 
##  56816.57  51520.52  56360.45  64004.97  67821.80  54058.58  44623.04  42645.21 
##       498       499       500       501       502       503       504       505 
##  42968.87  43761.15  38240.04  40642.38  45951.43  51641.12  52351.80  52462.51 
##       506       507       508       509       510       511       512       513 
##  46155.50  47497.38  43751.28  46510.65  46175.86  39882.72  41015.41  40189.38 
##       514       515       516       517       518       519       520       521 
##  41296.58  43940.35  36770.32  32042.99  55966.55  64138.81  67796.93  61166.63 
##       522       523       524       525       526       527       528       529 
##  62508.01  76112.90  83099.48  58014.24  52877.03  49566.68  53951.86  53457.49 
##       530       531       532       533       534       535       536       537 
##  43627.19  48626.07  61304.46  55817.59  59220.04  63213.44  60261.87  55285.61 
##       538       539       540       541       542       543       544       545 
##  48738.66  47392.66  55340.97  55091.04  47640.59  49867.35  49693.77  50388.58 
##       546       547       548       549       550       551       552       553 
##  41024.40  32848.65  37196.58  45324.60  45121.28  46829.56  40830.96  49856.60 
##       554       555       556       557       558       559       560       561 
##  51013.54  40778.35  50332.72  58206.56  57570.61  61173.37  56934.66  68711.68 
##       562       563       564       565       566       567       568       569 
##  85423.72  75500.82  64048.34  68473.75  66595.51  67783.81  59342.00  43308.65 
##       570       571       572       573       574       575       576       577 
##  50327.10  56240.48  57424.90  59503.33  60151.28  57273.69  69537.45  58896.00 
##       578       579       580       581       582       583       584       585 
##  52610.22  60222.39  61728.47  54812.87  61088.15  56658.11  53698.55  67287.57 
##       586       587       588       589       590       591       592       593 
##  52695.83  60050.31  59141.43  52854.91  52159.10  52435.52  43138.45  45939.18 
##       594       595       596       597       598       599       600       601 
##  40558.16  44809.86  53585.33  46907.29  52774.43  55155.53  60832.47  56985.27 
##       602       603       604       605       606       607       608       609 
##  61805.70  53362.64  55245.25  56022.66  58334.03  58908.87  58449.00  52618.35 
##       610       611       612       613       614       615       616       617 
##  59689.78  57747.19  54840.36  51541.89  44495.08  56021.29  59914.09  50836.96 
##       618       619       620       621       622       623       624       625 
##  61253.35  65444.08  58924.87  81183.13  66285.35  58604.08  60609.78  55956.68 
##       626       627       628       629       630       631       632       633 
##  46278.71  57000.89  37530.36  37390.40  46971.90  57469.02  55510.93  84279.53 
##       634       635       636       637       638       639       640       641 
##  74452.23  76656.00  78295.55  73014.62  65723.88  62353.55  50241.87  48607.95 
##       642       643       644       645       646       647       648       649 
##  47498.00  45978.17  44360.61  47041.13  51664.86  66656.24  81100.28  78219.70 
##       650       651       652       653       654       655       656       657 
##  79124.70  85004.21  98467.27  93218.71  63448.13  60896.45  57846.66  58832.64 
##       658       659       660       661       662       663       664       665 
##  55268.74  45668.38  48055.97  52379.66  51570.76  63148.33  63025.87  63315.79 
##       666       667       668       669       670       671       672       673 
##  51755.46  53116.84  54136.68  49469.09  43440.62  46479.66  44075.19  47566.73 
##       674       675       676       677       678       679       680       681 
##  45316.10  38143.94  32794.66  32792.16  35544.91  40283.36  42546.86  40548.66 
##       682       683       684       685       686       687       688       689 
##  40572.28  41021.65  35356.33  41694.34  41191.11  41490.78  43488.98  54211.28 
##       690       691       692       693       694       695       696       697 
##  62680.09  70740.25  60022.57  56024.91  52903.02  58064.10  48343.77  42034.25 
##       698       699       700       701       702       703       704       705 
##  35921.68  32636.33  31114.25  36628.36  34889.59  35563.15  41504.15  70202.52 
##       706       707       708       709       710       711       712       713 
##  76369.45  93940.38  90254.64  92853.43 107898.93 106738.00  84069.22  84417.00 
##       714       715       716       717       718       719       720       721 
##  75743.63  72842.42  70816.23  53522.60  48914.74  52411.53  49912.96  39013.46 
##       722       723       724       725       726       727       728       729 
##  44588.93  40855.20  43103.33  40975.67  31670.47  35626.51  36252.59  29879.59 
##       730       731       732       733       734       735       736       737 
##  47971.50  50222.13  48253.42  53914.87  46311.33  46586.25  54578.31  41011.78 
##       738       739       740       741       742       743       744       745 
##  37419.02  45931.56  42701.82  44206.41  46978.77  46090.76  42505.12  49503.20 
##       746       747       748       749       750       751       752       753 
##  44517.69  65507.68  70834.97  66939.17  58863.88  78668.95  71734.10  70651.90 
##       754       755       756       757       758       759       760       761 
##  55869.70 104653.82 121746.75 126341.74 107843.44 110274.90 109521.36 107548.02 
##       762       763       764       765       766       767       768       769 
##  60163.90  45195.38  47107.11  45729.86  43703.79 152417.09 156859.74 182096.94 
##       770       771       772       773       774       775       776       777 
## 185668.09 179597.88 177592.68 184483.79  81567.78  72144.74  38475.73  41913.16 
##       778       779       780       781       782       783       784       785 
##  42322.67  46726.09  41117.63  41438.04  41280.46  45839.96  40570.40  40267.49 
##       786       787       788       789       790 
##  45388.08  48416.75  46668.98  44014.83  46757.08 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8128
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original      bias    std. error
## t1*    4.941104   0.7633251    3.865037
## t2* 2557.341256 170.8395991  879.265829
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.027387       4.917448   12.86927
## 2    lag_depvar 1541.294040    2593.403819 4418.75306

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Dec 09 00:53:15 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Dec 09 00:53:22 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Dec 09 00:53:30 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Dec 09 00:53:38 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Dec 09 00:53:46 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Dec 09 00:53:54 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Dec 09 00:54:01 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Dec 09 00:54:09 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Dec 09 00:54:17 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Dec 09 00:54:25 2024
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_24 %>% 
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))
Item 2024 2023 2022 2021 2020
Agua 6.190545 5.195333 5.410333 5.849167 6.413450
Comida 329.464273 366.009167 312.386750 317.896583 338.247017
Comunicaciones 0.000000 0.000000 0.000000 0.000000 0.000000
Electricidad 88.688727 38.104750 47.072333 29.523000 43.320533
Enceres 26.169818 18.259750 24.219750 14.801167 24.056333
Farmacia 0.000000 10.704083 2.835000 13.996083 8.314383
Gas/Bencina 48.319273 42.636000 45.575000 13.583667 32.669133
Diosi 34.869000 55.804250 31.180667 52.687833 41.751467
donaciones/regalos 0.000000 0.000000 0.000000 0.000000 0.000000
Electrodomésticos/ Mantención casa 0.000000 0.000000 0.000000 0.000000 0.000000
VTR 17.992727 12.829167 25.156667 19.086917 18.535967
Netflix 1.517909 8.713833 7.151583 7.028750 6.506567
Otros 74.454545 5.481667 5.000000 0.000000 15.746333
Total 627.666818 563.738000 505.988083 474.453167 535.561183
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")

tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  47 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2535, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2024-12-09 00:04:58 sería de: 39.466 pesos// Percentil 95% más alto proyectado: 42.767,17

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 38485.83 38505.81
Lo.80 38592.15 38706.47
Point.Forecast 39465.64 42152.82
Hi.80 41324.69 46962.46
Hi.95 42343.59 49508.53


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(0,1,1) with drift 
## 
## Coefficients:
##           ma1   drift
##       -0.8839  5.7699
## s.e.   0.1020  3.3908
## 
## sigma^2 = 40236:  log likelihood = -456.72
## AIC=919.44   AICc=919.81   BIC=926.09
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(0,0,1) errors 
## 
## Coefficients:
##          ma1     xreg
##       0.3446  32.6260
## s.e.  0.1073   0.9385
## 
## sigma^2 = 36564:  log likelihood = -459.44
## AIC=924.88   AICc=925.25   BIC=931.58
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 985.2328 897.3811 834.2372
Lo.80 1122.4442 1043.2030 938.5374
Point.Forecast 1381.6428 1318.6671 1171.6384
Hi.80 1640.8413 1594.1312 1463.2301
Hi.95 1778.0527 1739.9531 1649.7874


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [4] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.9      
##  [7] tidytext_0.4.1      DT_0.32             janitor_2.2.0      
## [10] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [13] xts_0.13.2          forecast_8.21.1     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-11          
## [19] NLP_0.2-1           tsibble_1.1.4       lubridate_1.9.3    
## [22] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.2        
## [25] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [28] gsynth_1.2.1        lattice_0.20-45     GGally_2.2.1       
## [31] ggplot2_3.5.0       gridExtra_2.3       plotrix_3.8-4      
## [34] sparklyr_1.8.4      httr_1.4.7          readxl_1.4.3       
## [37] zoo_1.8-12          stringr_1.5.1       stringi_1.8.3      
## [40] data.table_1.15.0   reshape2_1.4.4      fUnitRoots_4021.80 
## [43] plyr_1.8.9          readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.2-0          systemfonts_1.0.5   selectr_0.4-2      
##   [4] lazyeval_0.2.2      websocket_1.4.1     crosstalk_1.2.1    
##   [7] listenv_0.9.1       digest_0.6.34       foreach_1.5.2      
##  [10] htmltools_0.5.7     fansi_1.0.6         ggfortify_0.4.16   
##  [13] magrittr_2.0.3      doParallel_1.0.17   tzdb_0.4.0         
##  [16] globals_0.16.2      vroom_1.6.5         sandwich_3.1-0     
##  [19] askpass_1.2.0       timechange_0.3.0    anytime_0.3.9      
##  [22] tseries_0.10-55     colorspace_2.1-0    xfun_0.42          
##  [25] crayon_1.5.2        jsonlite_1.8.8      iterators_1.0.14   
##  [28] glue_1.7.0          gtable_0.3.4        car_3.1-2          
##  [31] quantmod_0.4.26     abind_1.4-5         mvtnorm_1.2-4      
##  [34] DBI_1.2.2           rngtools_1.5.2      Rcpp_1.0.12        
##  [37] lfe_2.9-0           viridisLite_0.4.2   xtable_1.8-4       
##  [40] bit_4.0.5           Formula_1.2-5       htmlwidgets_1.6.4  
##  [43] timeSeries_4032.109 gplots_3.1.3.1      ellipsis_0.3.2     
##  [46] spatial_7.3-14      farver_2.1.1        pkgconfig_2.0.3    
##  [49] nnet_7.3-16         sass_0.4.8          dbplyr_2.4.0       
##  [52] chromote_0.2.0      utf8_1.2.4          labeling_0.4.3     
##  [55] tidyselect_1.2.0    rlang_1.1.3         later_1.3.2        
##  [58] munsell_0.5.0       cellranger_1.1.0    tools_4.1.2        
##  [61] cachem_1.0.8        cli_3.6.2           generics_0.1.3     
##  [64] evaluate_0.23       fastmap_1.1.1       yaml_2.3.8         
##  [67] processx_3.8.3      knitr_1.45          bit64_4.0.5        
##  [70] caTools_1.18.2      future_1.33.1       nlme_3.1-153       
##  [73] doRNG_1.8.6         slam_0.1-50         xml2_1.3.6         
##  [76] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.15.0  
##  [79] curl_5.2.0          bslib_0.6.1         highr_0.10         
##  [82] ps_1.7.6            fBasics_4032.96     Matrix_1.6-5       
##  [85] its.analysis_1.6.0  urca_1.3-3          vctrs_0.6.5        
##  [88] pillar_1.9.0        lifecycle_1.0.4     lmtest_0.9-40      
##  [91] jquerylib_0.1.4     bitops_1.0-7        R6_2.5.1           
##  [94] promises_1.2.1      KernSmooth_2.23-20  janeaustenr_1.0.0  
##  [97] parallelly_1.37.0   codetools_0.2-18    ggstats_0.5.1      
## [100] assertthat_0.2.1    boot_1.3-28         gtools_3.9.5       
## [103] MASS_7.3-54         openssl_2.1.1       withr_3.0.0        
## [106] fracdiff_1.5-3      parallel_4.1.2      hms_1.1.3          
## [109] quadprog_1.5-8      timeDate_4032.109   rmarkdown_2.25     
## [112] snakecase_0.11.1    carData_3.0-5       TTR_0.24.4
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))